Different Modality Based Remote Sensing Data Fusion Approach for Efficient Classification of Agriculture and Urban Subclasses

Chaudhri, Shiv Nath; Rajput, N. S.; Singh, Kumar Hemant; Singh, Dharmendra

doi:10.1109/igarss.2019.8899201

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…The SC [43] is an unsupervised approach for learning sets of overcomplete bases to represent data efficiently to find a set of basis vectors which can be used to represent an input vector as a linear combination of these basis vectors. Deep learning approach showed good potential for detecting heavy metal content in lettuce leaves Classification of corn seedling (W22, BxM, B73, PH207, Mo17) cold damage [54] Ten-layer CNN model Spectral analysis and CNN model could provide useful reference for detecting cold damage in corn seedlings Detection of aflatoxin in peanuts [55] Five-layer CNN architecture Approach gave recognition rates of 96% and 90% on pixel and kernel levels Classification of agriculture and urban subclasses [56] CNN model and two modalities (hyperspectral and LiDAR)…”

Section: Deep Learning Techniques For Hyperspectral Data Analyticsmentioning

confidence: 99%

“…Their approach gave recognition rates of 96% and 90% on pixel and kernel levels respectively, and gave better results compared with traditional classifiers such as KNN, SVM and BP-ANN. The authors in [56] applied the deep learning algorithm based on CNN to classify agriculture and urban subclasses. The authors considered two modalities, hyperspectral data and LiDAR data in their work.…”

Section: Deep Learning Techniques For Hyperspectral Data Analyticsmentioning

confidence: 99%

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Big Data and Machine Learning With Hyperspectral Information in Agriculture

2021

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Hyperspectral and multispectral information processing systems and technologies have demonstrated its usefulness for the improvement of agricultural productivity and practices by providing useful information to farmers and crop managers on the factors affecting crop status and growth. These technologies are widely used in a range of agriculture applications such as crop management, crop yield forecasting, crop disease detection, and the monitoring of agriculture land usage, water, and soil conditions. Hyperspectral information sensing can acquire several hundred spectral bands that cover the electromagnetic spectrum of an observational scene in a single acquisition. The resulting hyperspectral data cube contains a large volume of spatial and spectral information. The hyperspectral sequence of images or video further increases the data generation velocity and volume which lead to the Big data challenges particularly in agricultural remote sensing applications. This paper is structured to first give a comprehensive review of representative studies to provide insights into significant research efforts in agriculture using Big data, machine learning and deep learning with the focus on frameworks or architectures, information processing and analytics with hyperspectral and multispectral data. The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising. The paper then further explores the potential of using ensemble machine learning and scalable parallel discriminant analysis which takes into consideration the spatial and spectral components for Big data in agriculture. To the best of our knowledge, no similar review study on agriculture with Big data, machine learning and deep learning for hyperspectral and multispectral information processing has been reported. Furthermore, the potential of ensemble machine learning and scalable parallel discriminant analysis has not been explored in agriculture information processing. Experiments and data analytics have been performed on hyperspectral data from agriculture for validation. The results have shown the good performance of our approach.

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Section: Deep Learning Techniques For Hyperspectral Data Analyticsmentioning

confidence: 99%

Section: Deep Learning Techniques For Hyperspectral Data Analyticsmentioning

confidence: 99%

Big Data and Machine Learning With Hyperspectral Information in Agriculture

2021

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“…Data (hyperspectral and LiDAR) are then processed with convolutional neural network (CNN). For better agriculture planning, a CNN-based procedure is projected so that arrangement of objects can be done efficiently (Chaudhri et al, 2019).…”

Section: Introduction and Discussionmentioning

confidence: 99%

Advance control strategies using image processing, UAV and AI in agriculture: a review

Syeda

Alam

Illahi

et al. 2021

WJE

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Purpose The purpose of this paper is to provide an overview of smart agriculture systems and monitor and identify the technologies which can be used for deriving traditional agriculture system to modern agriculture system. It also provides the reader a broad area to work for the advancement in the field of agriculture and also explains the use of advanced technologies such as spectral imaging, robotics and artificial intelligence (AI) in the field of agriculture. Design/methodology/approach Smart uses of modern technologies were reviewed in the field of agriculture, which helps to monitor stress levels of plants and perform operations according to requirements. Operations can be irrigation, pests spray, monitoring crops, monitoring yield production, etc. Based on the literature review, a smart agriculture system is suggested. The parameters studied were spectral image processing, AI, unmanned aerial vehicle (UAVs) (fixed and rotatory), water or soil moisture, nutrients and pesticides. Findings The use of autonomous vehicles and AI techniques has been suggested through which the agriculture system becomes much more efficient. The world will switch to the smart agriculture system in the upcoming era completely. The authors conclude that autonomous vehicle in the field of science is time-saving and health efficient for both plants and workers in the fields. The suggested system increases the productivity of crops and saves the assets as well. Originality/value This review paper discusses the various contemporary technologies used in the field of agriculture and it will help future researchers to build on this research. This paper reveals that the UAVs along with multispectral, hyperspectral or red, green and blue camera (depends on the need) and AI are more suitable for the advancement of agriculture and increasing yield rate.

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